Prediction of Maize Price using Hidden Markov
Chain Model: An Application on Grand market of
Lome (TOGO)

Marc Marin Agbodjan; Joseph Mung’atu; Anthony Wanjoya

doi:https://doi.org/10.14445/22315373/IJMTT-V68I6P514

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 68 | Issue 6 | Year 2022 | Article Id. IJMTT-V68I6P514 | DOI : https://doi.org/10.14445/22315373/IJMTT-V68I6P514

Prediction of Maize Price using Hidden Markov Chain Model: An Application on Grand market of Lome (TOGO)

Marc Marin Agbodjan, Joseph Mung’atu, Anthony Wanjoya

Received	Revised	Accepted	Published
26 Apr 2022	04 Jun 2022	09 Jun 2022	28 Jun 2022

Citation :

Marc Marin Agbodjan, Joseph Mung’atu, Anthony Wanjoya, "Prediction of Maize Price using Hidden Markov Chain Model: An Application on Grand market of Lome (TOGO)," International Journal of Mathematics Trends and Technology (IJMTT), vol. 68, no. 6, pp. 117-130, 2022. Crossref, https://doi.org/10.14445/22315373/IJMTT-V68I6P514

Abstract

Around the world, the Hidden Markov Models (HMM) are the most popular methods in the machine learning and statistics for modeling sequences, especially in speech recognition domain. According to the number of patent applications for speech recognition technology form 1988 to 1998, the trend shows that this method has become very mature. The Hidden Markov Model (HMM) is a stochastic model where the modeled system is considered as a Markov process with unknown parameters. The challenge is to determine the hidden parameters of the observed parameters. It provides a probabilistic framework for time series forecast modeling. If the causal factors are not directly observed and have the properties of the Markov chain, therefore, a pair of observations and its causative factors are hidden Markov model. The proposed model was applied to a variation in the price of cereal products on the Togolese market between January 2015 and December 2021. The price of these cereal products was influenced by several factors, such as the international recession, government policy, climate, etc. The Forward-Backward, Viterbi and Baum-Welch algorithm was used to estimate the estdimated parameters were used to calculate the expected state of price for those grain product to find the most likely sequence and our proposed model would be coded using Matlab.

Keywords

Hidden Markov Chain(HMM), Transition Probability Matrix, Emission Probability, Maize, Estimated parameters

References

[1] Nurul Nnadiah Zakaria,Mahmod Othman,Rajalingam Sokkalingam,Hanita Daud,Lazim Abdullah and Evizal Abdul Kadir(2019), "Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak" MDPI.
[2] Aparna Bairagi, "Markov Chain Modelling for Prediction on Future Market Price of Potatoes with Special Reference to Nagaon District." IOSR Journal of Business and Management (IOSRJBM) 19.12 (2017): 25-31
[3] Gongsin Isaac Esbond, Fumilayo W. O. Saporu, Introducing the Use of Markov Chain Model for Informed Decision Making for Investors in a Stock Market Environment, International Journal of Science and Research (IJSR)
[4] F. O. Mettle, Enoch Quay, Laryea Ravenhill, (2014), A methodology for stochastic analysis of share prices as Markov chains with finite states, SpringerPlus 3(1):657
[5] Otieno S, Otumba EO, Nyabwanga RN, Application of Markov Chain to Model and Forecast Stock Market Trend: A Study of Safaricom Shares in Nairobi Securities Exchange, Kenya, International Journal of Current Research. Vol. 7, Issue, 04, PP. 14712-14721, April; 2015
[6] Zhu. X. And Xu. X. (2012).“Markov chain analysis and prediction on the fluctuation cycle of vegetable price”, Journal of System and Management Sciences ISSN 1816-6075 (Print), 1818-0523 (Online), Vol. 2 (2012) No. 3, pp. 40-49
[7] Jasinthan, P., Laheetharan. A. And Satkunanathan. N. “A Markov Chain Model for Vegetable Price Movement in Jaffna.” Sri Lankan Journal of Applied Statistics, Vol. (16-2) , IASSL, ISSN 2424-6271 93
[8] Gwan Seon Kim , Mehdi Nemati , Steven Buck , Nicholas Pates and Tyler Mark (2020),Recovering Forecast Distributions of Crop Composition: Method and Application to Kentucky Agriculture, MDPI journals
[9] AGBAM, Azubuike Samuel (F,cfia; F,cilrm) (2020), Application of Markov Chain (MC) Model to the Stochastic Forecasting of Stocks Prices in Nigeria: The Case Study of Dangote Cement, International Journal of Applied Science and Mathematical Theory,Vol 6. No.1 2020
[10] Zhang D, Zhang X., Study on Forecasting the Stock Market Trend Based on Stochastic Analysis Method, International Journal of Business and Management. Vol. 4, No. 6. June; 2009
[11] Felix Okoe Mettle, Enoch Nii Boi Quaye and Ravenhill Adjetey Laryea, A methodology for stochastic analysis of share prices as Markov chains with finite states, Mettle et al. a SpringerOpen Journal 2014, 3:657
[12] Lyndhurst Collins, An Introduction to Markov Chains Analysis, https://ibis.geog.ubc.ca/courses/ geob479/spatial_analysis/1-intro-markov-chain-analysis2.pdf
[13] Analisa Teknik Pengenalan Sinyal Wicara Dengan , (2008).Hidden Markov Model Neural Network. Jurnal litek , vol 5, no 2, 64-67
[14] Marwala, (2000).Probabilistic System Analyses: Part I. University of Witwatersrand, Johannesburg
[15] Andersson Cuellar, Josephine and Fransson, Linus. 2016.Algorithmic Trading Based on Hidden Markov Models-Hidden Markov Models as a Forecasting Tool When Trying to Beat the Market. Bachelor’s thesis. University of Gothenburg. Available: https://gupea. ub.gu.se/bitstream/2077/44767/1/gupea207744767